mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-09-23 10:30:03 +00:00
4f59693207
* [fx] compute memory stat and flop count for MetaInfoProp. * [fx] modify node attribute. * [fx] modify ckpt_chen. * [fx] fix compatibility. * [fx] fix import error. * [fx] skip test for MetaInfoProp. * [fx] skip test for MetaInfoProp. * [fx] skip test for MetaInfoProp. * [fx] skip test for MetaInfoProp. * [fx] skip if torch 1.11.0. * [fx] recover MetaInfoProp support for PyTorch 1.11. * [fx] provide a stable but not accurate enough version of profiler. * [fx] provide a stable but not accurate enough version of profiler. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix import error.
23 lines
1011 B
Python
23 lines
1011 B
Python
from typing import Tuple
|
|
import torch
|
|
from ..registry import meta_profiler_module
|
|
|
|
|
|
@meta_profiler_module.register(torch.nn.AvgPool1d)
|
|
@meta_profiler_module.register(torch.nn.AvgPool2d)
|
|
@meta_profiler_module.register(torch.nn.AvgPool3d)
|
|
@meta_profiler_module.register(torch.nn.MaxPool1d)
|
|
@meta_profiler_module.register(torch.nn.MaxPool2d)
|
|
@meta_profiler_module.register(torch.nn.MaxPool3d)
|
|
@meta_profiler_module.register(torch.nn.AdaptiveAvgPool1d)
|
|
@meta_profiler_module.register(torch.nn.AdaptiveMaxPool1d)
|
|
@meta_profiler_module.register(torch.nn.AdaptiveAvgPool2d)
|
|
@meta_profiler_module.register(torch.nn.AdaptiveMaxPool2d)
|
|
@meta_profiler_module.register(torch.nn.AdaptiveAvgPool3d)
|
|
@meta_profiler_module.register(torch.nn.AdaptiveMaxPool3d)
|
|
def torch_nn_pooling(self: torch.nn.Module, input: torch.Tensor) -> Tuple[int, int]:
|
|
# all pooling could be considered as going over each input element only once (https://stackoverflow.com/a/67301217)
|
|
flops = input.numel()
|
|
macs = 0
|
|
return flops, macs
|